Structure−Odor Relationships: Using Neural Networks in the Estimation of Camphoraceous or Fruity Odors and Olfactory Thresholds of Aliphatic Alcohols

Abstract

Structure-odor relationships were established for a sample of 99 aliphatic alcohols using a three-layer backpropagation neural network. The molecular structure was described using a common skeleton with six possible substitutions. Substituents were described using only their van der Waals volumes. The discrimination between fruity and camphoraceous odors of 67 compounds gave good results in classification (100%) and prediction (85%) phases. With the global set, the network correctly classified and predicted the camphoraceous character of compounds (100% and 95% respectively) but gave poorer results for the fruity character (87% and 74% respectively). Calculations of pOLs (pOL = -log (olfactory threshold expressed in mol/L)) of 45 camphoraceous compounds were also made. When all camphoraceous compounds were used to establish the model, 91% of the pOLs were correctly estimated. When attempts were made to predict the pOL values of 10% of the compounds from a model designed using 90% of the sample, only 74% of the pOLs were correctly estimated.